{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T17:28:50Z","timestamp":1785432530826,"version":"3.56.0"},"reference-count":43,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2026,7,18]],"date-time":"2026-07-18T00:00:00Z","timestamp":1784332800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"King Faisal University","award":["KFU263606"],"award-info":[{"award-number":["KFU263606"]}]},{"award":["KFU263606"],"award-info":[{"award-number":["KFU263606"]}],"id":[{"id":"https:\/\/ror.org\/00dn43547","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>Background\/Objectives: Glaucoma is a leading cause of irreversible blindness, and it is hard to catch early be-cause it rarely causes symptoms until real damage has already occurred. Existing au-tomated detection methods still miss the subtle structural changes that show up before vision loss begins. This study presents an automated framework based on optic nerve structure and retinal biomarkers, aimed at enabling earlier and more consistent glau-coma screening. The framework combines segmentation and classification in a single deep learning pipeline, trained and tested on the ORIGA and REFUGE2 fundus image datasets. Each image is resized, denoised with median filtering, and contrast-enhanced through histogram equalisation and normalisation, then cropped down to the optic nerve region using central cropping and intensity-based localisation. Methods: A custom encod-er\u2013decoder CNN segments the optic disc and cup, and from that segmentation, we ex-tract biomarkers ophthalmologists already rely on, including the vertical cup-to-disc ratio and disc\/cup area measurements. A Lightweight Vision Transformer separately learns broader structural patterns across the retina, and the two feature sets are fused and passed through a SoftMax classifier. Results: Segmentation accuracy was strong: Dice scores of 0.9082 for the optic disc and 0.9994 for the optic cup, IoU scores of 0.8351 and 0.9988, and an overall mean Dice of 0.9538 and mean IoU of 0.9169. Classification performance held up well, too, with high F1-score, recall, accuracy, and precision across normal and glaucomatous cases. Conclusions: The combination of interpretable, clinically established biomarkers with transformer-based global feature learning gives the framework the ability to support automated glaucoma risk assessment and demon-strates promising performance for glaucoma detection using retinal fundus images.<\/jats:p>","DOI":"10.3390\/jcm15145651","type":"journal-article","created":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T07:54:13Z","timestamp":1784534053000},"page":"5651","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Lightweight Vision Transformer and Retinal Biomarker Fusion Framework for Early Glaucoma Detection: Toward Improved Clinical Screening"],"prefix":"10.3390","volume":"15","author":[{"given":"Alifa","family":"Nasrin","sequence":"first","affiliation":[{"name":"Combined Military Hospital (CMH), Chattagram 4220, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad Bin","family":"Asif","sequence":"additional","affiliation":[{"name":"Clinical Department, Azerbaijani Medical University, Baku AZ1022, Azerbaijan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1452-8992","authenticated-orcid":false,"given":"Fatima Tuz","family":"Zahra","sequence":"additional","affiliation":[{"name":"Management Information System, College of Business Administration, King Faisal University, Al-Ahsa 31982, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Afzal Haq","family":"Asif","sequence":"additional","affiliation":[{"name":"Pharmacy Practice, College of Clinical Pharmacy, King Faisal University, Al-Ahsa 31982, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gausul Azam","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of Clinical Nutrition, College of Applied Medical Sciences, King Faisal University, Al-Ahsa 31982, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5069-0447","authenticated-orcid":false,"given":"Md","family":"Arifuzzaman","sequence":"additional","affiliation":[{"name":"Civil and Environmental Engineering, King Faisal University, Al-Ahsa 31982, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0793-7763","authenticated-orcid":false,"given":"Ramasamy","family":"Naidu","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, College of Computer Sciences & Information Technology, King Faisal University, Al-Ahsa 31982, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"AKM","family":"Azad","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 13318, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3420-3597","authenticated-orcid":false,"given":"Muhammad Ali","family":"Martuza","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, College of Computer, Qassim University, Buraydah 51452, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,18]]},"reference":[{"key":"ref_1","first-page":"25158414241287431","article-title":"Early diagnostics and interventional glaucoma","volume":"16","author":"Bacharach","year":"2024","journal-title":"Ther. 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